Animal Feed Science and Technology, 179: 24-35 (2013)

Effects of feeding nutritionally balanced rations on animal productivity, feed conversion efficiency, feed nitrogen use efficiency, rumen microbial protein supply, parasitic load, immunity and enteric methane emissions of milking animals under field conditions

M.R. Garga,*, P.L. Sherasiaa, B.M. Bhanderia, B.T. Phondbaa, S.K. Shelkea and H.P.S. Makkarb

aAnimal Nutrition Group, National Dairy Development Board, Anand 388 001, Gujarat, India;

 bAnimal Production and Health Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla 00153 Rome, Italy

 

Abstract

Milking animals produce milk commensurate with their genetic potential only when they are fed a nutritionally balanced ration in an amount that provides nutrients to express their genetic potential. As animals kept by smallholder farmers are rarely fed a balanced ration, a programme to feed balanced rations to animals of such farmers was launched in India. Based on their milk yield, the animals were categorized as: low (<8 kg/d), medium (8 to 12 kg/d) and high (>12 kg/d) yielders. Milk yield, milk fat and net daily income to milk producers were recorded before and after feeding a balanced ration. Nutritional status of animals showed that, for 71% of animals’, crude protein (CP) and metabolizable energy intakes were higher and, for 65% of animals’, calcium and phosphorus intakes were lower than requirements. Ration balancing improved milk yield by 2 to 14% and its milk fat proportion by 0.2 to 15%. Feed conversion efficiency, as kg of fat corrected milk (FCM)/kg of dry matter intake of buffaloes (n=1,131) before and after feeding balanced rations was 0.6 and 0.7, respectively, and in cows (n=540) the values were 0.6 and 0.8. Dietary N secreted into milk increased from 0.16 to 0.25 and 0.16 to 0.19 in low and medium yielding cows and buffaloes, respectively. Rumen microbial CP synthesis also increased (P<0.05) by 36 and 38% in cows and buffaloes, respectively. On feeding balanced rations, levels (mg/ml) of plasma immunoglobulins IgG, IgM and IgA increased from 14.48 to 22.11, 2.69 to 3.29 and 0.48 to 0.67, and the parasitic load was reduced from 168 to 81 eggs/g of faeces. Enteric CH4 emissions (g/kg milk yield) was reduced by 15 to 20% (P<0.05) in these lactating animals. Results demonstrate that feeding  nutritionally balanced rations increased milk production and reduced enteric CH4 emissions and N excretion from lactating cows and buffaloes. While implementation of a ration balancing programme under small holding systems is challenging,  large scale use of this programme in tropical countries can help improve productivity of milking animals with available feed resources in an environmentally sustainable manner.

Keywords: Balanced feeding; Productivity; Feed conversion efficiency; Methane emission; Milking animals

 

Abbreviations: BUN, blood urea N; BW, body weight; Ca, calcium; CP, crude protein; DM, dry matter; FCE, feed conversion efficiency; FCM, fat corrected milk; INAPH, Information Network for Animal Productivity and Health; LRP, Local Resource Person; ME, metabolizable energy; N, nitrogen; NDP, National Dairy Plan; P, phosphorus; RBP, Ration Balancing Programme; SF6, sulfur hexafluoride.

1.    Introduction

Dairying is acknowledged as one of the major instruments which can bring socio-economic transformation to the rural poor in India. The Indian dairy sector acquired substantial growth momentum during the last three decades and achieved an annual output of 122.8 million tonnes of milk in 2010/11 (MOF, 2010). This has made the country the highest milk producer in the world and provided milk and milk products for the burgeoning population. Domestic Indian demand for milk and milk products is projected at 200 to 210 million tonnes by 2021/22 (Anonymous, 2011). To meet the growing demand for milk, the National Dairy Development Board (NDDB) of India rolled out a National Dairy Plan (NDP), spanning a 15 yr period, with the first phase of 6 yr called ‘National Dairy Support Programme’, starting in 2012. The aim of this phase is to increase productivity of food animals through breed improvement and a scientific approach to balanced feeding.

The breed improvement programme needs to be supported by feeding the animals a balanced ration commensurate with their genetic potential. Milking animals in India are usually fed one or two locally available concentrate feed ingredients such as oilseed cakes or meals with brans to supplement grasses and crop residues. This often leads to feeding of a nutritionally imbalanced ration which contains proteins, energy, minerals and vitamins either in excess or shortage relative to the nutrient requirements of the animals. Imbalanced feeding adversely impacts productivity, health and welfare of animals, as well as the quality and safety of animal products and increases the environmental impact. In addition, income of farmers from milk production is adversely affected since up to 0.7 of the total cost of milk production is feed.

Feeding nutritionally balanced rations in big organized farms in developed countries has been the practice for decades. However to implement the concept of nutritionally balanced rations to smallholder farmers has always been a challenge owing to their lack of knowledge and skills. Smallholder farmers are also not in a fiscal position to hire specialists that could assist them in preparing balanced rations. The NDDB has developed user-friendly ration balancing software for preparing least cost balanced rations for dairy cattle using locally available feed resources. This software program is the backbone of the Ration Balancing Programme (RBP). Local Resource Persons (LRP) provide training on use of this programme and they advise milk producers on preparation of balanced rations using locally available feed resources. The programme has been pilot tested on a large number of milking animals, and suggested that feeding nutritionally balanced rations in smallholder dairy settings increases net daily income by increasing daily milk yield, and its milk fat level, while decreasing the cost of feeding. In addition, emissions of enteric CH4, and excretion of nitrogen in manure, decreased and feed conversion efficiency increased. These results have considerable implications for resource use efficiency in developing countries and in meeting the challenges being imposed by ongoing climate change. In view of this, this study was undertaken to assess impacts of feeding nutritionally balanced rations on animal productivity, feed conversion efficiency, feed N use efficiency, rumen microbial crude protein (MCP) supply, parasitic load, immunity and enteric CH4 emissions of milking animals under field conditions.

2.    Materials and methods

To determine the impacts of feeding nutritionally balanced rations to lactating cows and buffaloes under field conditions, studies were conducted in different agro-climatic regions of India. The approach and methodology is described below.

2.1.       Development of the Ration Balancing Programme

The NDDB has developed an Information Network for Animal Productivity and Health (INAPH), a windows based Internet linked application designed to assess the prevailing nutritional status of an animal’s diet versus its nutrient requirements. Both sets of information are used to determine a least cost ration with the available feed resources and an area specific mineral mixture. The software is compatible with desktops, laptops and netbooks, and can be used on Personal Digital Assistants for areas devoid of Internet connectivity.

The RBP is comprised of a feed data library and various ‘Nutrition masters’. To create the feed data library, a wide range of feed ingredients such as green and dry forages, tree leaves, grains, oil cakes and agro-industrial byproducts were collected from different agro-ecological zones of the country and analyzed for chemical composition. Simultaneously, existing national and international feeding standards for nutrient requirement of growing, lactating and pregnant animals were used to create various ’Nutrition masters’ of nutrient requirements (Kearl, 1982; NRC, 2001).

2.2.       Implementation of the RBP

In India, average dairy animal herd size is 2 to 3 milking animals/household. Under Indian conditions of feeding and management, more reliable data is available on individual animals than on farm systems. The RBP has been implemented using individual animal profiles and hence data generated was based on individual animals. Through various implementing agencies with adequate infrastructure and manpower (addressed as End Implementing Agencies; EIAs) NDDB has initiated multi-state implementation of the RBP on a large scale across the country.  The EIAs could be dairy cooperative societies (DCS), service providing organizations, state animal husbandry departments, producer companies and Non-Government Organizations. The NDDB imparts training to identified technical officers and trainers of the implementing agencies relative to the latest concepts of animal nutrition and the RBP software.

2.2.1.      Selection of Local Resource Persons

 Prospective LRP candidates submitted a completed application form to the DCS which shortlisted and recommended potential candidates who wrote a test and were orally interviewed. The identified LRPs were trained in groups of 10 to 15 for 2 wks as one week of theoretical training and one week of in-field training. The DCS technical officers and supervisory staff regularly monitored the work completed by the LRPs and progress of the RBP. The LRPs were paid a full allowance during the first year and 50% allowance during the second year, but later became self-sustaining as they received commissions from sale of feeds, feed supplements and mineral mixtures to the milk producers.  If there was any change in the supply or availability of feeds, the farmers contact the LRP and a ration was re-formulated.

2.3.       Steps for creating least cost rations for various categories of animals using the software

2.3.1.   Registration of animals

Animals identified for the RBP were ear tagged with a unique 12 digit number by LRP. Details of the animal, such as species, breed, age, milking status (lactating/dry), calving number, last calving date and pregnancy status were recorded. The owner’s profile (i.e., name, father’s name, age, village, village institution, ‘tehsil’ (zone), district and state) was also recorded. After providing all the information, the animal was registered on the server.

2.3.2.   Assessing nutrient status of animals

After registration, the animal’s daily feed intake including refusals, daily milk yield, milk fat level, body weight (BW) and pregnancy status were recorded by LRP on a structured datasheet prepared in local languages. Using the information in the datasheet the software calculated the nutrient requirements of the animal. Based on the current feeding practices (i.e., feed intake, feed ingredients, ration fed), the status of nutrient provision to the animal in terms of metabolizable energy (ME), CP and essential minerals was assessed in order to better understand the deficiencies/excesses of nutrients in the ration and the cost of milk production.

2.3.3.   Formulating least cost ration using locally available feed resources

Based on the chemical composition of available feed resources, and in accordance with the nutrient requirements of the animal, the software computed the least cost ration, which was provided to the farmer.

2.3.4.    Selection of animals and their management 

A total of 438 villages were selected from the Indian states of Andhra Pradesh, Bihar, Gujarat, Rajasthan and Uttar Pradesh. From these villages, 12,518 early lactation cows and buffaloes were selected and milk yield, milk fat level and net daily income to farmers before and after implementation of the RBP were recorded. Among the selected cows and buffaloes, the majority were housed in a well-ventilated paddock, while some were kept under the shade of trees in natural conditions. Management conditions before and after implementation of the RBP were similar.

The BW of the animals was calculated using Shaeffer’s formula as:

BW (kg) = ([(heart girth (cm)/2.54)2 x length of the body (cm)/2.54]/300) x 0.4536.

The LRP collected milk samples for analysis of milk fat and measured daily milk yield, feed ingredients fed and feed refusals, recorded animal and owner profiles and, based on current feeding information, formulated nutritionally balanced rations by re-adjusting the available feed resources at least cost using the ration balancing software. The formulated ration was provided to the farmers in a prescribed format in the local language. The LRP visited each farmer after 3 to 4 wks, or whenever there was a change in the feed ingredients, to re-formulate a balanced ration. They also ensured that the farmers fed the prescribed ration to their animals. All data were stored on a central server.

2.4.    Measurement of milk yield and milk fat

The LRP measured daily milk yield before and after feeding the balanced rations by visiting individual farmers. Milk samples were collected and analyzed for milk fat level by MilkoTester (Rajasthan Electronics and Instruments Ltd., Jaipur, India), located at the village dairy cooperative society (DCS). Milk yield and milk fat level recorded before feeding the balanced ration was utilized for formulation of a balanced ration. All cows and buffaloes were categorized according to production level as low (< 8 kg/d), medium (8 to 12 kg/d) and high yielders (> 12 kg/d).  

2.5.    Feed conversion efficiency

It was defined as ‘kg milk of standardised composition with respect to fat concentrations produced per kg feed DM consumed’ and was determined using data from 540 cows and 1,131 buffaloes.

2.6.    Efficiency of feed nitrogen utilization

Efficiency of utilization of feed N for milk production was defined as conversion of feed N into milk N calculated as the quantity of N in milk expressed as a proportion of feed N intake. Data from 439 cows and 721 buffaloes were used for this calculation.

2.7.    Microbial CP synthesis

Urine samples (100 ml) were collected from 55 cows and 26 buffaloes before and after feeding the balanced rations and preserved with sufficient quantity of 1.87 mol/L H2SO4 to maintain pH below 3. The urine samples were assayed for allantoin, uric acid and creatinine (Young and Conway, 1942; Hawk et al. 1976). The purine derivative excretion was measured by spot sample test on the basis of creatinine as an internal marker (Chen et al. 1992). The daily excretion of creatinine was considered as 0.98 mmol/kg W0.75 (Makkar, 2004). Purines absorbed and microbial N supplies were calculated from daily urinary purine derivative excretion (IAEA, 1997).

2.8.    Blood parameters

Blood samples were collected prior to feeding from the jugular veins of individual animals (n=34) into air-tight vacutainer tubes with EDTA. Plasma was prepared after centrifugation of the blood at 1000 xg for 5 min at 23oC and frozen for later blood biochemical analyses. Samples were analyzed for the immunoglobulins IgG, IgM and IgA using a kit supplied by DiaSys Diagnostic Systems GmbH (Holzheim, Germany); and blood urea N (BUN) was determined using the method of Rahmatullah and Boyde (1980).

2.9.    Faecal samples

Fresh faecal samples were collected before and after feeding the balanced rations from 34 cows and analyzed for parasitic egg counts. The McMaster technique (Henriksen and Aagaard, 1976) was used to prepare faeces for quantification of worm eggs and results are expressed as number of eggs/g wet faeces.

2.10.  Methane emission measurements

For measurement of enteric CH4 emissions, five field experiments were conducted on 73 cows and 61 buffaloes selected from villages in 4 Indian states. Selected cows and buffaloes were in their 2nd to 4th lactation. Methane measurement was by the sulfur hexafluoride (SF6) tracer technique (Johnson et al., 1994). Two researchers and one technical staff from NDDB were responsible for each field experiment and stayed at the village during the experiment. The DM intake, milk yield and milk fat were recorded daily during the CH4 gas sampling period. Representative samples of feeds were collected for chemical analysis. Thereafter, rations of all animals were balanced for ME, CP and essential minerals using the software described earlier.

To measure CH4, breath samples of all experimental animals were collected daily for 4 consecutive days in canisters by the researchers and brought to the laboratory for CH4 and SF6 analyses at the start of study. After 30 d of feeding the balanced rations, CH4 emissions were measured again for 4 consecutive days. Methane and SF6 concentrations were determined by gas chromatography (Perkin Elmer AutoSystem XL, Model Clarus 500), and the CH4 emission rate was calculated as the product of the permeation tube emission rate and the ratio of CH4 to SF6 concentration in the sample. Samples were analyzed in triplicate. Standards were used to standardize the gas chromatograph for SF6 (39.2 pptv and 101.7 pptv, Scott-Marrin Inc., Riverside, CA, USA) and CH4 (10.4 ppmv and 101.9 ppmv, Scott-Marrin Inc., Riverside, CA, USA).

Methane emission rate was calculated as:

Q CH4 = Q SF6 x (CH4) / (SF6)

where: Q CH4 = CH4 emission rate (g/min); Q SF6 = Known release rate of SF6 from permeation tube (g/min); CH4 = CH4 concentration of collected sample in the canister (µg/m3); and SF6 = SF6 concentration of collected sample in canister (µg/m3).

 

2.11.  Statistical analysis

Statistical analysis of the data was by Student’s ‘t’ test as per Snedecor and Cochran (1986) with the SPSS package (1999).

3.  Results

 

3.1.    Nutritional status of the animals

The nutritional status data of 12,518 lactating cows and buffaloes were created by calculating requirements versus intake using the RBP software. About 71% of the animals had excess CP and ME intakes compared to requirements, while 13% had deficient intakes of both. The extent of over-feeding for CP and ME was 65 and 51%, respectively in both species. Calcium and P intake was deficient in 65% of the animals, with the extent of under-feeding being 33 and 42% for Ca and P, respectively. Only 11% of the animals had excess intakes of Ca and P.

3.2.    Milk yield and milk fat

The RBP implemented showed that there was an increase in daily milk yield from 0.2 to 1.2 kg and fat level in milk from 1.0 to 9.0 g/kg in cows and buffaloes (Table 1). Feeding the balanced ration to low yielding cows and buffaloes increased (P<0.05) milk yield and its milk fat level. Medium yielding buffaloes had improved (P<0.05) milk yield and milk fat level, while cows only had higher (P<0.05) milk yield. Milk fat level of high yielding buffaloes increased (P<0.05) after feeding balanced rations. The ration cost decreased by 5 to 11% in cows and buffaloes fed balanced rations. There was an average increase in net daily income of farmers by 6 to 60% per animal due to the increase in milk yield and milk fat level, as well as decrease in cost of feeding.

3.3.    Efficiency of feed nutrient utilization

Feed conversion efficiency (FCE) measures conversion efficiency of nutrients into milk and is measured as kg of FCM produced per kg of DM consumed. Feeding balanced rations improved FCE and profit due to increase in milk and fat yield. The FCE of FCM (kg/kg DM intake) of low (n=206), medium (n=322) and high (n=12) yielding cows before and after feeding balanced rations were 0.4, 0.6, 0.8 and 0.7, 0.8, 1.0, respectively. Similarly, in low (n=457), medium (n=617) and high (n=57) yielding buffaloes, FCE were 0.4, 0.6, 0.7 and 0.6, 0.7, 0.8, respectively (Table 2). After feeding balanced rations, efficiency of utilization of dietary N for milk production also improved (P<0.01) in low and medium yielding cows and buffaloes. The average proportion of dietary N secreted into milk increased from 0.16 to 0.25 and 0.16 to 0.19 in low and medium yielding cows (n=428) and buffaloes (n=666), respectively (Table 3).

3.4.    Microbial CP synthesis

Rumen microbial CP yield (g/d) increased (P<0.05) by 39 and 44% in cows and buffaloes, respectively, after feeding balanced rations. Similarly, efficiency of microbial CP synthesis increased (P<0.05) by 36 and 38% in cows and buffaloes respectively, after feeding balanced rations (Table 4).

3.5.    Blood parameters

After feeding balanced rations to dairy cows, levels (mg/ml plasma) of immunoglobulins such as IgG, IgM and IgA increased from 14.5 to 22.1 (P<0.01), 2.7 to 3.3 (P<0.05) and 0.48 to 0.67, respectively (Table 5). There was no change in urea N, creatinine and uric acid levels in blood plasma.

3.6.    Faecal egg counts

The intensity of infection as faecal egg counts ranged from 80 to 280 and 20 to 120/g of wet faeces before and after feeding of balanced rations. By feeding balanced rations to the animals, average eggs/g faeces was reduced from 168 to 81 (Table 5).

3.7.    Methane emission measurements

The CH4 emissions from lactating animals after feeding balanced rations was 177.3 g/d which was lower (P<0.05) than emissions prior to feeding the balanced rations (Table 6). Methane emissions (g/kg milk yield) declined (P<0.05) in Gujarat and Uttar Pradesh states by 17 to 20% and 17 to 21% in cows and buffaloes, respectively. In Andhra Pradesh and Maharashtra states, the CH4 emissions (g/kg milk yield) were reduced (P<0.05) by 13 to 15% in cows and buffaloes.

4.  Discussion

This study has demonstrated successful application of a ration balancing approach on a large number of smallholder farms in India. This is a unique study as it involved monitoring of impacts of a ration balancing approach using sound scientific principles on a large number of animals located in a wide variety of locations which, unlike controlled research, is more complex in nature and difficult to conduct. Information on effects of feeding balanced rations on milk production, profitability to farmers, rumen microbial CP synthesis, efficiency of utilization of dietary CP for milk production, feed conversion efficiency, level of plasma immunoglobulins, faecal parasitic load and enteric CH4 emissions in lactating cows and buffaloes under field conditions were collected.

Our outcomes can help policy makers, science managers, regulators, administrators and researchers for large scale implementation of such programmes in developing countries. Considering its importance and relevance, the concept of ration nutritional balancing can be applied to various production systems, including lactating and beef animals. The information obtained may also prove useful for researchers, field veterinarians and service providing organizations having village level networks in order to convince milk producers for the benefits of nutritionally balanced feeding.

4.1.    Nutritional status of animals                                                                             

The lack of differences in nutrient intake between cows and buffaloes (Figures 1 and 2) suggests that under field conditions these animal groups are equally treated in allocation of feed resources.

In order of priority, available good quality feed resources are first allocated to lactating animals followed by dry pregnant animals, dry animals, heifers, growing calves and non-productive animals. Our data indicates that, unlike ME and CP, which were in excess in the ration of 71% of the animals, Ca and P were deficient in the ration of about 65% of animals because of low mineral mixture feeding, probably due to high costs. Milk producers in most developing countries often do not feed adequate quantities of mineral mixtures to their animals due to non-availability, lack of knowledge on the benefits of feeding mineral mixtures or high cost. In the Eastern states of India, dietary ME may be in excess, with CP, Ca and P deficient, but the nutritional status of milking animals with regard to these nutrients vary among regions depending upon local feeding practices. Nevertheless, our results suggest that it is possible to reduce costs of rations by decreasing levels of ME and CP, while increasing milk yield and/or milk fat level, to increase profits. In situations (39%) where CP and ME are deficient with Ca and P, there will be an increase in feeding costs but milk production and milk fat level will increase. Farmers often have strong preferences for feeding regionally available feed ingredients, unmindful of chemical composition and cost. For example in the eastern states of India, farmers traditionally mainly feed crushed maize grains to their animals (Ramachandra et al., 2007), which may be a good source of ME but can lead to a deficiency of CP and minerals in the diet.

4.2.    Feed conversion efficiency and feed nitrogen use efficiency

Cost of feeding is the single largest component in the total cost of milk production. In developing countries, where the level of production of milking animals is not high and the returns from milk production are marginal, this is primarily due to higher consumption of DM per liter of milk production. Through balanced feeding it was possible to increase the FCE for milk production in cows and buffaloes to produce more milk per kg DM. This is useful to increase the profitability of milk producers, and contributes to efficient use of scarce feed resources in developing countries while achieving targeted milk production. Achieving higher milk production from the same amount of feeds would also decrease water and carbon footprints of animal products because the water footprint of feed production is very high (Mekonnen and Hoekstra, 2012) and fossil fuel energy is utilized in production of feed biomass (Flachowsky and Kamphues, 2012).  Shahjalal et al. (2000) and Castillo et al. (2001) also reported increased FCE for milk production by balancing the ration of milking animals.

Inefficiencies in conversion of feed CP into edible animal protein present a major challenge to livestock nutritionists, as excretion by the animal of excess dietary N is an economic loss of a valuable feed component and a source of nitrate in waste water which can cause toxicity and pollution, aerosol ammonia emissions which cause animal and human welfare and air pollution problems, as well as nitrous oxide which is a potent greenhouse gas. In our study, secretion of milk CP increased in low and medium yielding cows and buffaloes following balancing of rations, suggesting that dietary N was utilized better for milk protein synthesis and that losses of N in urine and faeces was reduced. A number of studies have shown that reducing the CP content of the diet, above that needed to meet animal requirements, leads to better efficiency of N use for milk production and a lesser proportion excreted in urine and faeces (Krober et al., 2000; Kulling et al., 2001; Broderick, 2003; Rotz, 2004).

In our study, over all N use efficiency after ration balancing was 23% versus 19% before implementation, clearly demonstrating the benefits of our ration balancing approach. A recent study from FAO indicates that the calculated feed N use efficiency for milk production in 60% of global lactating cows is <0.1; and in Africa and Asia 92% and 15% respectively of the lactating cows have calculated feed N use efficiency for milk production of <0.1 (Powell et al., 2000). IPCC (2006) assumes 0.2 feed N use efficiency for milk production worldwide. In developed countries, feed N use efficiency for milk production is 0.25 to 0.35 (Powell et al., 2006; IFCN, 2011; Wilkinson, 2011; Gourley et al., 2012). Our measured feed N use efficiency for milk was 0.19 for low, medium and high yielding cows and buffaloes before feeding balanced rations, which is higher than the 0.11 to 0.13 feed N use efficiency for milk calculated from data collected on smallholder dairy farms in Uttar Pradesh in India (Powell, 2010), but lower than the 0.29 to 0.34 (Kaushal et al., 2011) determined under experimental conditions. Feed N use efficiency for milk at 0.23 after ration balancing was closer to experimental conditions, but still lower, suggesting that the potential exists to further enhance nutrient use efficiency.

4.3.    Milk yield, milk fat and milk production efficiency

The improvement in milk yield and milk fat level in cows and buffaloes after feeding a nutritionally balanced ration could be due to increased rumen microbial CP synthesis due to more optimal rumen function because of the more balanced nutrient supply. Haldar and Rai (2003) also reported an improvement in milk yield due to supplementation of an energy/ mineral mixture in lactating ruminants. Supplementation of minerals to the diet of lactating ruminants has been reported to enhance milk production and milk composition (Dutta et al., 2010; Khochare et al., 2010).

4.4.    Microbial protein synthesis and enteric methane emission

Microbial CP flow to the duodenum is an important indicator of efficiency of rumen function. Urinary excretion of allantoin has been successfully used to estimate microbial CP synthesized in the rumen and subsequently digested in the lower gut of ruminants (Pimp et al., 2001; Dipu et al., 2006; Ramgaokar et al., 2008; Srinivas and Singh, 2010). A number of studies (Makkar and Chen, 2004) show that supply of adequate nutrients increases excretion of urinary purine derivatives, synthesis of rumen microbial CP and enhances the supply of protein post-ruminally to support production.

Microbial CP synthesis depends upon a balanced ruminal supply of ammonia, energy and carbon skeletons for amino acid synthesis (Wanapat et al., 2009; Khandaker et al., 2012). A deficiency of feed N leads to spillage of ATP released on digestion of feed C and suboptimum synthesis of microbial CP and release of feed C as CH4, while a deficiency of feed C results in non-assimilation of ammonia by rumen microbes, again resulting in sub-optimum microbial CP synthesis (Blummel et al., 2010). In addition, ammonia in the rumen passes into the blood through the rumen wall and is detoxified by the liver to urea in an energy demanding process (Weijs et al., 1996; Huntington and Archibeque, 1999), which will decrease animal productivity. In our study, baseline BUN levels were within the normal range, although 71% of the animals were supplied diets excess in CP and ME. The high ME level could be responsible for better utilization of CP which allowed BUN levels to be within normal ranges.

An inverse relationship between microbial CP production, and its efficiency of production, with CH4 emissions have been reported (Blummel, 2000; Waghorn and Hegarty, 2011). In addition, a reduction in CH4 emissions with supplementation of urea molasses blocks has also been reported (Mohini and Singh, 2010), and feeding as per the nutrient requirements of animals to improve feed utilization and enhance overall production efficiency of the herd was found to be an effective way to reduce CH4 emissions in dairy cattle in the USA (Capper et al., 2009) and in beef cattle in Canada (GHGMP, 2005).

4.5.      Blood parameters

Imbalances of dietary nutrients can alter activity of enzymes thereby impairing immune function (Spears, 2000). Functionality of numerous structural proteins, enzymes and cellular proteins depends on nutrients, including minerals, being ingested in appropriate amounts (NRC, 2001; Nocek et al., 2006). Our results, showing that feeding balanced rations to lactating cows and buffaloes helped to improve overall immune status, is consistent with Wedekind et al. (1992) who showed that a mineral mixture in the ration of dairy animals increased IgG production and affected cell metabolism resulting in better immune status. Subclinical or marginal deficiencies of minerals may be a larger problem than an acute deficiency (Garg et al., 2007) because specific signs of deficiency are not evident as the animal continues to grow, produce and reproduce at reduced levels (Larson, 2005).

4.6.    Faecal egg counts

Increased availability of essential nutrients can be expected to improve host resistant to gastrointestinal nematodes provided that they are first limiting to immune functions (Houdijk, 2012). Animals fed an imbalanced diet are vulnerable to parasitic infestation due to lower host immunity (Athanasiadou et al., 2009), and the parasitic load in dairy animals affects growth, milk production (Fekete and Kellems, 2007) and general health as these parasites use essential nutrients supplemented in the diet. Better nutrition has been shown to reduce parasitic load through improvement in immunity of animals (Hoste et al., 2005; Houdijk, 2012).

5.  Conclusions

Our results demonstrate that by nutritionally balancing the ration of animals under field conditions using locally available feed resources and mineral mixtures, it was possible to increase daily milk yield and/or milk fat level as well as the profitability of the farms. It was also possible to reduce the cost of feeding per kg of milk production in many cases. In addition, balanced feeding also helped in reducing CH4 emissions per kg of milk, improve immunity and reduce incidences of parasitism. These effects may have been elicited by increases in rumen microbial CP synthesis as a result of better rumen function.

The study suggests that increases in demand for animal products in developing countries can be met by augmenting milk and meat production if available feed resources are utilised efficiently. This study has shown for the first time that agencies having village level network can provide ration balancing advisory services to smallholder farmers, on a large scale, resulting in enhanced animal productivity and resource use efficiency while simultaneously, curtailing livestock mediated pollutants such as N in manure and CH4 in air.

Mixed farming systems are the most important systems worldwide since they produce about 90% of the milk and about 70% of sheep and goat meat and beef (Steinfeld et al., 2006). The majority of farmers in mixed farming systems are smallholder poor farmers who lack resources and skills to nutritionally balance their animals’ diets. A ration balancing programme as we describe could likely be replicated in other developing countries and may be a powerful way to improve productivity of smallholder farmers in a sustainable manner while realizing the full genetic potential of farm animals to lift smallholder farmers out of poverty.

Acknowledgements

Financial assistance and facilities provided by the management of National Dairy Development Board, Anand, for undertaking this study are gratefully acknowledged. Efforts of all officers of Animal Nutrition Group of National Dairy Development Board and that of participating unions are duly acknowledged for providing necessary support in implementing this programme at the farm level.

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Table 1

Effect of ration balancing (RB) on milk production and milk fat level.

 

Species                         Milk yield*

Milk yield (kg/d)

Milk fat (g/kg)

Before RB

After RB

P

Before RB

After RB

P

Andhra Pradesh (N=23)

Cows (n=430)

Low

6.2 ± 0.26

6.6 ± 0.24

0.23

42.5 ± 0.06

42.7 ± 0.06

0.80

Medium

9.5 ± 0.23

10.0 ± 0.29

0.18

40.3 ± 0.05

40.4 ± 0.06

0.91

Bihar (N=43)

Cows (n=758)

Low

5.6 ± 0.15

5.9 ± 0.16

0.24

41.6 ± 0.09

44.4 ± 0.09

0.03

Medium

9.1 ± 0.23

9.6 ± 0.28

0.14

41.0 ± 0.08

42.8 ± 0.07

0.14

High

13.3 ± 0.56

13.8 ± 0.55

0.53

38.6 ± 0.08

39.8 ± 0.07

0.28

Buffaloes  (n=717)

Low

4.9 ± 0.28

5.1 ± 0.25

0.51

62.2 ± 0.25

71.7 ± 0.25

0.01

Medium

10.9 ± 0.34

11.1 ± 0.36

0.65

59.1 ± 0.09

68.3 ± 0.10

≤ 0.01

Gujarat (N=76)

Cows (n=2167)

Low

6.2 ± 0.04

6.6 ± 0.04

≤ 0.01

39.2 ± 0.02

41.5 ± 0.03

≤ 0.01

Medium

8.6 ± 0.03

8.9 ± 0.03

≤ 0.01

39.6 ± 0.02

41.7 ± 0.02

≤ 0.01

High

13.3 ± 0.32

13.7 ± 0.33

0.29

41.5 ± 0.10

45.4 ± 0.13

0.03

Buffaloes  (n=5077)

Low

6.6 ± 0.02

7.0 ± 0.02

≤ 0.01

65.6 ± 0.02

68.7 ± 0.02

≤ 0.01

Medium

8.7 ± 0.02

9.0 ± 0.02

≤ 0.01

65.9 ± 0.01

68.6 ± 0.01

≤ 0.01

High

12.5 ± 0.10

12.9 ± 0.10

0.03

66.4 ± 0.08

69.3 ± 0.08

0.01

Rajasthan (N=23)

Cows (n=750)

Low

7.0 ± 0.31

7.8 ± 0.26

0.05

40.5 ± 0.13

42.4 ± 0.12

0.27

Buffaloes  (n=856)

Low

5.6 ± 0.16

6.3 ± 0.17

0.01

58.2 ± 0.06

64.4 ± 0.07

≤ 0.01

Medium

8.8 ± 0.21

9.3 ± 0.22

0.15

53.1 ± 0.11

60.4 ± 0.10

≤ 0.01

Uttar Pradesh (N=162)

Cows (n=914)

Low

5.5 ± 0.24

6.2 ± 0.30

0.06

42.2 ± 0.13

44.4 ± 0.12

0.27

Medium

9.4 ± 0.28

10.7 ± 0.37

0.01

37.7 ± 0.15

42.0 ± 0.15

0.06

High

14.0 ± 0.71

14.8 ± 0.97

0.48

41.3 ± 0.22

45.9 ± 0.22

0.16

Buffaloes (n=849)

Low

5.5 ± 0.30

6.3 ± 0.29

0.07

62.4 ± 0.21

67.1 ± 0.19

0.13

Medium

9.0 ± 0.20

10.3 ± 0.27

≤ 0.01

61.4 ± 0.28

65.6 ± 0.29

0.39

Total cows (n=5019)

Low

6.1 ± 0.04

6.6 ± 0.04

≤ 0.01

41.2 ± 0.02

43.1 ± 0.02

0.01

Medium

9.2 ± 0.03

9.8 ± 0.04

0.05

39.7 ± 0.02

41.7 ± 0.02

0.09

High

13.5 ± 0.25

14.1 ± 0.28

0.08

40.5 ± 0.07

43.7 ± 0.07

0.08

Total buffaloes (n=7499)

Low

5.7 ± 0.02

6.2 ± 0.02

0.02

62.1 ± 0.02

68.0 ± 0.02

0.02

Medium

9.4 ± 0.02

9.9 ± 0.02

0.03

59.9 ± 0.01

65.7 ± 0.01

0.02

High

12.5 ± 0.09

12.9 ± 0.10

0.10

66.4 ± 0.07

69.3 ± 0.07

0.01

Total (N=327) (n=12,518)

8.1 ± 0.02

8.4 ± 0.02

≤ 0.01

58.8 ± 0.02

61.5 ± 0.02

≤ 0.01

 

Low: < 8 kg/d; Medium: 8 to 12 kg/d; High: > 12 kg/d. N    Number of villages in a state. n          Total number of animals.

 

 

Table 2

Effect of ration balancing (RB) on milk production efficiency (± SE).

 

Cows of different milk yield*

Buffaloes of different milk yield*

Low  (n=206)

Medium (n=322)

High (n=12)

Low (n=457)

Medium (n=617)

High (n=57)

Milk yield (kg/d)

Before RB

6.3 ± 0.06a

8.8 ± 0.05a

14.1 ± 0.65a

6.1 ± 0.05a

8.9 ± 0.04a

12.9 ± 0.18a

After RB

6.7 ± 0.07b

9.1 ± 0.05b

14.6 ± 0.59a

6.7 ± 0.06b

9.2 ± 0.04b

13.1 ± 0.16b

Milk fat (g/kg)

Before RB

40.7 ± 0.04a

40.4 ± 0.03a

41.0 ± 0.21a

66.1 ± 0.04a

65.3 ± 0.03a

70.4 ± 0.13a

After RB

42.8 ± 0.04b

42.3 ± 0.03b

43.3 ± 0.26a

70.1 ± 0.04b

68.7 ± 0.04b

71.5 ± 0.13b

FCM (kg/d)

Before RB

6.4 ± 0.07a

8.8 ± 0.07a

14.3 ± 0.89a

6.5 ± 0.05a

9.4 ± 0.05a

14.4 ± 0.26a

After RB

7.0 ± 0.08b

9.5 ± 0.08b

15.4 ± 0.98a

7.4 ± 0.06b

10.1 ± 0.05b

14.9 ± 0.33a

DM intake (kg/d)

Before RB

14.9 ± 0.25a

16.3 ± 0.20a

18.8 ± 1.76a

15.4 ± 0.17a

17.4 ± 0.18a

20.6 ± 0.62a

After RB

10.4 ± 0.08b

11.5 ± 0.06b

15.3 ± 0.58a

12.7 ± 0.09b

14.8 ± 0.08b

19.0 ± 0.27a

Feed conversion efficiency

Before RB

0.4 ± 0.01a

0.6 ± 0.01a

0.8 ± 0.09a

0.4 ± 0.01a

0.6 ± 0.01a

0.7 ± 0.03a

After RB

0.7 ± 0.01b

0.8 ± 0.01b

1.0 ± 0.05a

0.6 ± 0.01b

0.7 ± 0.01b

0.8 ± 0.02b

 

*Low: < 8 kg/d; Medium: 8 to 12 kg/d; High: > 12 kg/d. FCM Fat corrected milk; 40 g/kg FCM and 60 g/kg FCM has been considered for cows and buffaloes, respectively. DM: Dry matter. a, b   Values with different superscript in a column within respective parameter differ (P<0.01).

 

Table 3

Effect of ration balancing (RB) on the efficiency of utilization of dietary N for milk production.

 

Cows of different milk yield

Buffaloes of different milk yield

Low (n=148)

Medium (n=280)

High (n=11)

Low (n=171)

Medium (n=495)

High (n=55)

Milk yield (kg/animal/d)

Before RB

6.3 ± 0.06a

8.8 ± 0.05a

14.2 ± 0.64a

6.5 ± 0.50a

9.0 ± 0.04a

12.9 ± 0.19a

After RB

6.7 ± 0.06b

9.1 ± 0.06b

14.7 ± 0.65b

7.0 ± 0.50b

9.3 ± 0.04b

13.1 ± 0.17b

CP intake (g/animal/d)

Before RB

1390 ± 25.0a

1625 ± 21.8a

1930 ± 24.1a

1487 ± 24.1a

1702 ± 12.9a

2095 ± 71.7a

After RB

931 ± 15.9b

1146 ± 10.0b

1619 ± 9.2a

1335 ± 12.0b

1629 ± 7.0b

2141 ± 36.8a

Average milk CP output (g/animal/d)

Before RB

208.8 ± 1.93a

289.3 ± 1.78a

468.0 ± 2.13a

228.6 ± 1.82a

314.2 ± 1.41a

451.2 ± 6.53a

After RB

221.9 ± 2.01b

299.4 ± 1.86b

484.8 ± 2.14b

244.6 ± 1.92b

326.6 ± 1.44b

458.6 ± 6.09b

Dietary N secreted into milk

Before RB

0.15 ± 0.003a

0.18 ± 0.003a

0.24 ± 0.005a

0.15 ± 0.002a

0.18 ± 0.001a

0.21 ± 0.006a

After RB

0.24 ± 0.002b

0.26 ± 0.001b

0.29 ± 0.002a

0.18 ± 0.006b

0.20 ± 0.009b

0.21 ± 0.004a

 

a, b      Values with different superscript in a column within respective parameter differ (P<0.01).

CP, Crude protein. Low: < 8 kg/d; Medium: 8 to 12 kg/d; High: > 12 kg/d.  Milk protein: 33 g/kg and 35 g/kg in cows and buffaloes, respectively.

 

Table 4

Effect of ration balancing (RB) on efficiency of microbial CP synthesis.

 

	Cows (n=55)		Buffaloes (n=26)
	Before RB	After RB	Before RB	After RB
Microbial yield (g CP/d)	724.1a ± 6.32	1004.4b ± 10.95	485.6a ± 4.21	697.6b ± 10.21
Efficiency of microbial protein synthesis (g CP/kg DOM)	68.3a ± 4.83	93.3b ± 6.24	56.6c ± 3.39	78.3d ± 6.71

 a, bValues within a species with different superscripts in a row differ (P<0.05). CP: Crude protein; DOM: Digestible organic matter.

 

Table 5

Effect of feeding balanced ration on various parameters in cows (n=34).

 

	Before Ration Balancing	After Ration Balancing
Milk yield (kg/d)	11.0 ± 0.68	11.6 ± 0.53
Fat (g/kg)	39.0 ± 0.07	39.6 ± 0.08
Protein (g/kg)	31.6 ± 0.02	32.3 ± 0.03
Blood plasma
IgG (mg/ml)	14.5a ± 0.32	22.1b ± 0.41
IgM (mg/ml)	2.7c ± 0.03	3.3d ± 0.04
IgA (mg/ml)	0.5 ± 0.01	0.7 ± 0.01
BUN (mg/dl)	12.1 ± 0.55	12.3 ± 0.67
Urine
Allantoin (mmol/L)	11.7a ± 0.63	17.1b ± 0.78
Uric acid (mmol/L)	1.3 ± 0.15	1.6 ± 0.12
Creatinine (mmol/L)	7.0 ± 0.59	6.8 ± 0.53
Faeces
Eggs/g faeces	168a ± 1.7	81b ± 1.6

a, b Values with different superscripts in a row differ (P<0.01).

c, d Values with different superscripts in a row differ (P<0.05).

BUN: Blood urea N.

 

 

Table 6

Effect of ration balancing (RB) on milk production and CH4 emissions.

		Milk yield (kg/d)	Fat (g/kg)	CH4 emissions (g/d)	CH4 emissions (g/kg milk)
Gujarat State
Cows (n=30)	Before RB	11.9a ± 0.35	40.8a ± 0.09	224.6a ± 4.37	19.3a ± 2.24
	After RB	12.4b ± 0.31	42.8b ± 0.10	198.1b ± 1.50	16.3b ± 1.96
Buffaloes (n=22)	Before RB	8.5a ± 0.49	65.0a ± 0.08	232.5a ± 5.93	27.3a ± 1.59
	After RB	8.9b ± 0.40	68.3b ± 0.09	199.6b ± 4.98	22.4b ± 2.41
Uttar Pradesh State
Cows (n=13)	Before RB	4.9a ± 0.13	42.0a ± 0.09	195.8a ± 5.74	39.5a ± 1.74
	After RB	5.5b ± 0.11	43.9b ± 0.06	173.5b ± 4.75	31.5b ± 1.67
Buffaloes (n=13)	Before RB	5.2a ± 0.14	59.8a ± 0.09	214.7a ± 7.10	40.9a ± 2.20
	After RB	5.9b ± 0.13	6.3.2b ± 0.08	192.2b ± 5.93	32.4b ± 1.98
Andhra Pradesh State
Cows (n=30)	Before RB	8.4a ± 0.43	41.0 ± 0.05	187.3a ± 4.56	22.2a ± 2.41
	After RB	8.8b ± 0.42	41.4 ± 0.05	166.4b ± 3.93	18.8b ± 1.80
Maharashtra State
Buffaloes (n=26)	Before RB	6.1c ± 0.35	64.8c ± 0.16	154.5c ± 5.46	25.3a ± 1.59
	After RB	6.6d ± 0.37	67.7d ± 0.13	133.9d ± 5.37	20.4b ± 1.35

  a, b             Values for a parameter, before and after RB and within a species with different superscripts in a column differ (P<0.05).

  c, d     Values for a parameter, before and after RB and within a species with different superscripts in a column differ (P<0.01).